The Identification of Influential Subsets in Regression Using a Fuzzy Clustering Strategy

Abstract

A fuzzy clustering strategy is used to identify subsets of influential observations in regression. As part of the fuzzy clustering strategy, the analyst can explore the uniqueness of selected subsets and the degree of belonging of observations to selected subsets. This is accomplished by either varying a fuzzy parameter or the number of clusters. Once the observations or subsets have been identified, the analyst can then compute regression diagnostics to confirm their degree of influence in regression. Bootstrapping and high-breakdown procedures were used to confirm the influence of the previously identified subsets. This fuzzy clustering strategy is applied to the modified data on wood-specific gravity and an augmented production dataset. Both datasets have been previously presented in the literature.

KEY WORDS:

• Bootstrapping
• Fuzzy K-means clustering
• High-breakdown procedures
• Influential subsets in regression